jupyterlab-pachyderm
A JupyterLab extension for integrations with Pachyderm.
This extension is composed of a Python package named jupyterlab_pachyderm
for the server extension and a NPM package named jupyterlab-pachyderm
for the frontend extension.
Requirements
-
JupyterLab >= 3.0
-
Python >=3.8,<4
- pyenv is a great way to manage and install different versions of python. You can check which version you are using by running
pyenv versions
. Our Python extension is built to be compatible with Python versions 3.8 to 3.10. Therefore, it is best to run the lowest version (3.8.x) for highest compatibility.
-
Node
- If you are using nvm first run
nvm install
. This will install and switch the version of node to the one defined in the .nvmrc
. If you are upgrading the version of node used in the project, please check and make sure that the versions defined in the .nvmrc
.
Development Environment
There are two ways you can work on this project. One is setting up a local python virtual environment and the other is using a docker image developing on a virtual machine.
Dev Container Workflow
Building and running the extension in a Docker container allows us to use mount, which is not possible in recent versions of macOS.
Assuming that you are running a Pachyderm instance somewhere, and that you can port-forward pachd
kubectl port-forward service/pachd 30650:30650
Start a bash session in the pachyderm/notebooks-user
container from the top-level jupyterlab-pachyderm
repo.
docker run --name jupyterlab_pachyderm_frontend_dev \
-p 8888:8888 \
-it -e GRANT_SUDO=yes --user root \
--device /dev/fuse --privileged \
-v $(pwd):/home/jovyan/extension-wd \
-w /home/jovyan/extension-wd \
pachyderm/notebooks-user:<latest master SHA> \
bash
If you are running the frontend container on Linux, and want to be able to talk to pachd in minikube on grpc://localhost:30650
, use --net=host
at the start of the the docker run
command as shown above. If you are on macOS, you will want to remove --net=host
as it is not supported. On macOS you can specify grpc://host.docker.internal:30650
to get the same effect.
Install the project in editable mode, and start JupyterLab
pip install -e ".[dev]"
jupyter labextension develop --overwrite
# Server extension must be manually installed in develop mode, for example
jupyter server extension enable jupyterlab_pachyderm
jupyter lab --allow-root
Open another bash inside the same container:
docker exec -it <container-id> bash
Within container run:
npm run watch
Note: Once run above, you can start the dev container again in future sessions using
docker start <container id>
and then running
docker exec -it <container id> bash
Local Virtual Environment Setup
When developing in python, it is good practice to set up a virtual environment. A simple guid to set up a virtual environment is as follows:
create a virtual environment using venv
python -m venv venv
Activate the environment
source venv/bin/activate
When you are done using the environment you can close your shell or deactivate the environment:
deactivate
Development install
Note: You will need NodeJS to build the extension package.
pip install -e ".[dev]"
jupyter labextension develop . --overwrite
jupyter server extension enable jupyterlab_pachyderm
npm run build
You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
npm run watch
jupyter lab --allow-root --ip=0.0.0.0
With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the npm run build
command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=False
Note that to enable debug logging for jupyter
(which also enables it for our extension backend, a python plugin to the JupyterLab server), you can run:
jupyter lab --debug
Development uninstall
jupyter server extension disable jupyterlab_pachyderm
pip uninstall jupyterlab_pachyderm
In development mode, you will also need to remove the symlink created by jupyter labextension develop
command. To find its location, you can run jupyter labextension list
to figure out where the labextensions
folder is located. Then you can remove the symlink named jupyterlab-pachyderm
within that folder.
Locally building the docker image
Useful if iterating on the Dockerfile locally.
Create & activate venv (using bazel):
bazel run //:venv
source venv/bin/activate
Build dist
directory:
python -m build
Build docker image:
export PACHCTL_VERSION=aaa7434c714fab6130c3982ebdaa8f279bd850c2 # or whichever version you want
docker build --build-arg PACHCTL_VERSION=$PACHCTL_VERSION -t pachyderm/notebooks-user:dev .
docker run -ti -p 8888:8888 pachyderm/notebooks-user:dev
Navigate to the URL that's printed out by the docker container, then change tree
to lab
in your browser's address bar.
Install
To install the extension, execute:
pip install jupyterlab_pachyderm
Uninstall
To remove the extension, execute:
pip uninstall jupyterlab_pachyderm
Server extension
First make sure the server extension is enabled:
jupyter server extension list 2>&1 | grep -ie "jupyterlab_pachyderm.*OK"
Note: Use any username and no password to login
API endpoints
GET /repos # returns a list of all repos/branches
GET /mounts # returns a list of all active mounts and unmounted repos/branches
PUT /_mount # mounts a single repo
PUT /_unmount # unmounts a single repo
PUT /_commit # commits any changes to the repo
PUT /_unmount_all # unmounts all repos
PUT /datums/_mount # mounts first datum of given input spec
PUT /datums/_next # mounts next datum
PUT /datums/_prev # mounts prev datum
GET /datums # returns info on mounted datum
The servers-side extension extends jupyter server, so it automatically starts as part of jupyter lab
.
API endpoints can be accessed via localhost:8888/pachyderm/v2
The frontend can access the endpoints via /v2
, for example:
requestAPI<any>('/v2/repos')
.then(data => {
console.log(data);
})
.catch(reason => {
console.error(reason);
});
You can also access it via localhost:8888/v2
SVG Images
We are leveraging svgr to simplify the use of non icon svgs in in the project. Svg images that are to be converted live in the svg-images
folder and get output to the src/utils/components/Svgs
folder. If you want to add a new image to the project simply add the svg to the svg-images
folder and run npm run build:svg
. We have spent some time trying to get svgr to work through the @svgr/webpack
plugin but have not been successful as of yet.
Project Structure
Jupyter extensions are composed of several plugins. These plugins can be selectively enabled or disabled. Because of this we have decided separate the functionality in the extension using plugins. Plugins exported in this extension are as follows.
Hub
This plugin contains custom styling and other features only used by hub. By default this extension is disabled.
Mount
This plugin contains the mount feature currently under development.
Plugin settings
You can disable certain plugins by specifying the following config data in the <jupyter_config_path>/labconfig/page_config.json
:
{
"disabledExtensions": {
"jupyterlab-pachyderm:examples": true
}
}
You can check your config paths by running jupyter --paths
. Setting this config file is not part of the built extension and needs to be done by the user.
Adding the following to the package.json in the jupyterlab
object will disable the examples plugin by default.
"disabledExtensions": ["jupyterlab-pachyderm:examples"]
So we can build the extension with hub features turned off and override the setting for hub.
Troubleshoot
If you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:
jupyter server extension list
If the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:
jupyter labextension list